Structural Learning in Object Oriented Domains. - Olav Bangsø; Helge Langseth; Thomas D. Nielsen (FLAIRS, 2001)

Heuristics for Two Extensions of Basic Troubleshooting. - Helge Langseth; Finn Verner Jensen (SCAI, 2001)

The SACSO methodology for troubleshooting complex systems. - Finn Verner Jensen; Uffe Kjærulff; Brian Kristiansen; Helge Langseth; Claus Skaanning; Jirí Vomlel; Marta Vomlelová (Artif. Intell. Eng. Des. Anal. Manuf., 2001)

Parameter Learning in Object-Oriented Bayesian Networks. - Helge Langseth; Olav Bangsø (Ann. Math. Artif. Intell., 2001)

Bayesian networks with applications in reliability analysis. - Helge Langseth (2002)

Fusion of Domain Knowledge with Data for Structural Learning in Object Oriented Domains. - Helge Langseth; Thomas D. Nielsen (J. Mach. Learn. Res., 2003)

Decision theoretic troubleshooting of coherent systems. - Helge Langseth; Finn Verner Jensen (Reliab. Eng. Syst. Saf., 2003)

Latent Classification Models. - Helge Langseth; Thomas D. Nielsen (Mach. Learn., 2005)

Classification using Hierarchical Naïve Bayes models. - Helge Langseth; Thomas D. Nielsen (Mach. Learn., 2006)

Bayesian networks in reliability. - Helge Langseth; Luigi Portinale (Reliab. Eng. Syst. Saf., 2007)

Maximum Likelihood Learning of Conditional MTE Distributions. - Helge Langseth; Thomas D. Nielsen; Rafael Rumí; Antonio Salmerón (ECSQARU, 2009)

Latent classification models for binary data. - Helge Langseth; Thomas D. Nielsen (Pattern Recognit., 2009)

Inference in hybrid Bayesian networks. - Helge Langseth; Thomas D. Nielsen; Rafael Rumí; Antonio Salmerón (Reliab. Eng. Syst. Saf., 2009)

Towards a More Expressive Model for Dynamic Classification. - Shengtong Zhong; Ana M. Martínez; Thomas D. Nielsen; Helge Langseth (FLAIRS, 2010)

Architectures Integrating Case-Based Reasoning and Bayesian Networks for Clinical Decision Support. - Tore Bruland; Agnar Aamodt; Helge Langseth (IIP, 2010)

Parameter estimation and model selection for mixtures of truncated exponentials. - Helge Langseth; Thomas D. Nielsen; Rafael Rumí; Antonio Salmerón (Int. J. Approx. Reason., 2010)

A hybrid CBR and BN architecture refined through data analysis. - Tore Bruland; Agnar Aamodt; Helge Langseth (ISDA, 2011)

Extended Abstract: Combining CBR and BN using metareasoning. - Tor Gunnar Houeland; Tore Bruland; Agnar Aamodt; Helge Langseth (SCAI, 2011)

Extended Abstract: A design for a tourist CF system. - Terje N. Lillegraven; Arnt C. Wolden; Anders Kofod-Petersen; Helge Langseth (SCAI, 2011)

A latent model for collaborative filtering. - Helge Langseth; Thomas Dyhre Nielsen (Int. J. Approx. Reason., 2012)

Mixtures of truncated basis functions. - Helge Langseth; Thomas D. Nielsen; Rafael Rumí; Antonio Salmerón (Int. J. Approx. Reason., 2012)

Effects of data cleansing on load prediction algorithms. - Boye Annfelt Høverstad; Axel Tidemann; Helge Langseth (CIASG, 2013)

Fast Approximate Inference in Hybrid Bayesian Networks Using Dynamic Discretisation. - Helge Langseth; David Marquez; Martin Neil (IWINAC (1), 2013)

Beating the bookie: A look at statistical models for prediction of football matches. - Helge Langseth (SCAI, 2013)

Learning to Rank for Personalised Fashion Recommender Systems via Implicit Feedback. - Hai Thanh Nguyen 001; Thomas Almenningen; Martin Havig; Herman Schistad; Anders Kofod-Petersen; Helge Langseth; Heri Ramampiaro (MIKE, 2014)

Requirement Engineering for a Small Project with Pre-Specified Scope. - Thomas D. Nielsen; Sigve Hovda; Antonio Fernández 002; Helge Langseth; Anders L. Madsen; Andrés R. Masegosa; Antonio Salmerón (NIK, 2014)

A New Method for Vertical Parallelisation of TAN Learning Based on Balanced Incomplete Block Designs. - Anders L. Madsen; Frank Jensen; Antonio Salmerón; Martin Karlsen; Helge Langseth; Thomas D. Nielsen (Probabilistic Graphical Models, 2014)

Learning mixtures of truncated basis functions from data. - Helge Langseth; Thomas D. Nielsen; Inmaculada Pérez-Bernabé; Antonio Salmerón (Int. J. Approx. Reason., 2014)

A classification-based approach to monitoring the safety of dynamic systems. - Shengtong Zhong; Helge Langseth; Thomas Dyhre Nielsen (Reliab. Eng. Syst. Saf., 2014)

Parallelisation of the PC Algorithm. - Anders L. Madsen; Frank Jensen; Antonio Salmerón; Helge Langseth; Thomas D. Nielsen (CAEPIA, 2015)

Parallel Importance Sampling in Conditional Linear Gaussian Networks. - Antonio Salmerón; Darío Ramos-López; Hanen Borchani; Ana M. Martínez; Andrés R. Masegosa; Antonio Fernández 002; Helge Langseth; Anders L. Madsen; Thomas D. Nielsen (CAEPIA, 2015)

Learning Conditional Distributions Using Mixtures of Truncated Basis Functions. - Inmaculada Pérez-Bernabé; Antonio Salmerón; Helge Langseth (ECSQARU, 2015)

MPE Inference in Conditional Linear Gaussian Networks. - Antonio Salmerón; Rafael Rumí; Helge Langseth; Anders L. Madsen; Thomas D. Nielsen (ECSQARU, 2015)

Modeling Concept Drift: A Probabilistic Graphical Model Based Approach. - Hanen Borchani; Ana M. Martínez; Andrés R. Masegosa; Helge Langseth; Thomas D. Nielsen; Antonio Salmerón; Antonio Fernández 002; Anders L. Madsen; Ramón Sáez (IDA, 2015)

Probability-based Approach for Predicting E-commerce Consumer Behaviour Using Sparse Session Data. - Øyvind H. Myklatun; Thorstein K. Thorrud; Hai Thanh Nguyen 001; Helge Langseth; Anders Kofod-Petersen (RecSys Challenge, 2015)

Dynamic Bayesian modeling for risk prediction in credit operations. - Hanen Borchani; Ana M. Martínez; Andrés R. Masegosa; Helge Langseth; Thomas D. Nielsen; Antonio Salmerón; Antonio Fernández 002; Anders L. Madsen; Ramón Sáez (SCAI, 2015)

Scalable learning of probabilistic latent models for collaborative filtering. - Helge Langseth; Thomas D. Nielsen (Decis. Support Syst., 2015)

Short-Term Load Forecasting With Seasonal Decomposition Using Evolution for Parameter Tuning. - Boye Annfelt Høverstad; Axel Tidemann; Helge Langseth; Pinar Öztürk (IEEE Trans. Smart Grid, 2015)

Parallel Filter-Based Feature Selection Based on Balanced Incomplete Block Designs. - Antonio Salmerón; Anders L. Madsen; Frank Jensen; Helge Langseth; Thomas D. Nielsen; Darío Ramos-López; Ana M. Martínez; Andrés R. Masegosa (ECAI, 2016)

Financial Data Analysis with PGMs Using AMIDST. - Rafael Cabañas 001; Ana M. Martínez; Andrés R. Masegosa; Darío Ramos-López; Antonio Salmerón; Thomas D. Nielsen; Helge Langseth; Anders L. Madsen (ICDM Workshops, 2016)

d-VMP: Distributed Variational Message Passing. - Andrés R. Masegosa; Ana M. Martínez; Helge Langseth; Thomas D. Nielsen; Antonio Salmerón; Darío Ramos-López; Anders L. Madsen (Probabilistic Graphical Models, 2016)

Scalable MAP inference in Bayesian networks based on a Map-Reduce approach. - Darío Ramos-López; Antonio Salmerón; Rafael Rumí; Ana M. Martínez; Thomas D. Nielsen; Andrés R. Masegosa; Helge Langseth; Anders L. Madsen (Probabilistic Graphical Models, 2016)

Data Driven Case Base Construction for Prediction of Success of Marine Operations. - Bjørn Magnus Mathisen; Agnar Aamodt; Helge Langseth (ICCBR (Workshops), 2017)

Bayesian Models of Data Streams with Hierarchical Power Priors. - Andrés R. Masegosa; Thomas D. Nielsen; Helge Langseth; Darío Ramos-López; Antonio Salmerón; Anders L. Madsen (ICML, 2017)

Content-Based Social Recommendation with Poisson Matrix Factorization. - Eliezer de Souza da Silva; Helge Langseth; Heri Ramampiaro (ECML/PKDD (1), 2017)

Inter-Session Modeling for Session-Based Recommendation. - Massimiliano Ruocco; Ole Steinar Lillestøl Skrede; Helge Langseth (DLRS@RecSys, 2017)

Scaling up Bayesian variational inference using distributed computing clusters. - Andrés R. Masegosa; Ana M. Martínez; Helge Langseth; Thomas D. Nielsen; Antonio Salmerón; Darío Ramos-López; Anders L. Madsen (Int. J. Approx. Reason., 2017)

A parallel algorithm for Bayesian network structure learning from large data sets. - Anders L. Madsen; Frank Jensen; Antonio Salmerón; Helge Langseth; Thomas D. Nielsen (Knowl. Based Syst., 2017)

MAP inference in dynamic hybrid Bayesian networks. - Darío Ramos-López; Andrés R. Masegosa; Ana M. Martínez; Antonio Salmerón; Thomas D. Nielsen; Helge Langseth; Anders L. Madsen (Prog. Artif. Intell., 2017)

Understanding and improving recurrent networks for human activity recognition by continuous attention. - Ming Zeng 009; Haoxiang Gao; Tong Yu 001; Ole J. Mengshoel; Helge Langseth; Ian R. Lane; Xiaobing Liu (UbiComp, 2018)

Effective hate-speech detection in Twitter data using recurrent neural networks. - Georgios K. Pitsilis; Heri Ramampiaro; Helge Langseth (Appl. Intell., 2018)

Detecting Offensive Language in Tweets Using Deep Learning. - Georgios K. Pitsilis; Heri Ramampiaro; Helge Langseth (CoRR, 2018)

Securing Tag-based recommender systems against profile injection attacks: A comparative study. - Georgios Pitsilis; Heri Ramampiaro; Helge Langseth (CoRR, 2018)

Scalable importance sampling estimation of Gaussian mixture posteriors in Bayesian networks. - Darío Ramos-López; Andrés R. Masegosa; Antonio Salmerón; Rafael Rumí; Helge Langseth; Thomas D. Nielsen; Anders L. Madsen (Int. J. Approx. Reason., 2018)

A deep network model for paraphrase detection in short text messages. - Basant Agarwal; Heri Ramampiaro; Helge Langseth; Massimiliano Ruocco (Inf. Process. Manag., 2018)

A Review of Inference Algorithms for Hybrid Bayesian Networks. - Antonio Salmerón; Rafael Rumí; Helge Langseth; Thomas D. Nielsen; Anders L. Madsen (J. Artif. Intell. Res., 2018)

New Ideas in Ranking for Personalized Fashion Recommender Systems. - Heri Ramampiaro; Helge Langseth; Thomas Almenningen; Herman Schistad; Martin Havig; Hai Thanh Nguyen 001 (Business and Consumer Analytics: New Ideas, 2019)

Forecasting Intra-Hour Imbalances in Electric Power Systems. - Tárik S. Salem; Karan Kathuria; Heri Ramampiaro; Helge Langseth (AAAI, 2019)

Securing Tag-based recommender systems against profile injection attacks: A comparative study. (Extended Report). - Georgios K. Pitsilis; Heri Ramampiaro; Helge Langseth (CoRR, 2019)

AMIDST: A Java toolbox for scalable probabilistic machine learning. - Andrés R. Masegosa; Ana M. Martínez; Darío Ramos-López; Rafael Cabañas 001; Antonio Salmerón; Helge Langseth; Thomas D. Nielsen; Anders L. Madsen (Knowl. Based Syst., 2019)

Prediction Intervals: Split Normal Mixture from Quality-Driven Deep Ensembles. - Tárik S. Salem; Helge Langseth; Heri Ramampiaro (UAI, 2020)

Analyzing concept drift: A case study in the financial sector. - Andrés R. Masegosa; Ana M. Martínez; Darío Ramos-López; Helge Langseth; Thomas D. Nielsen; Antonio Salmerón (Intell. Data Anal., 2020)

Learning similarity measures from data. - Bjørn Magnus Mathisen; Agnar Aamodt; Kerstin Bach; Helge Langseth (Prog. Artif. Intell., 2020)

Machine Learning in Financial Market Surveillance: A Survey. - Shweta Tiwari; Heri Ramampiaro; Helge Langseth (IEEE Access, 2021)

Correcting Classification: A Bayesian Framework Using Explanation Feedback to Improve Classification Abilities. - Yanzhe Bekkemoen; Helge Langseth (CoRR, 2021)

Probabilistic Models with Deep Neural Networks. - Andrés R. Masegosa; Rafael Cabañas 001; Helge Langseth; Thomas D. Nielsen; Antonio Salmerón (Entropy, 2021)

Detection of Potential Manipulations in Electricity Market using Machine Learning Approaches. - Shweta Tiwari; Gavin Bell; Helge Langseth; Heri Ramampiaro (ICAART (3), 2022)

A Reparameterization of Mixtures of Truncated Basis Functions and its Applications. - Antonio Salmerón; Helge Langseth; Andrés R. Masegosa; Thomas D. Nielsen (PGM, 2022)

A data-driven modular architecture with denoising autoencoders for health indicator construction in a manufacturing process. - Emil Blixt Hansen; Helge Langseth; Nadeem Iftikhar; Simon Bøgh (CoRR, 2022)

ASAP: Attention-Based State Space Abstraction for Policy Summarization. - Yanzhe Bekkemoen; Helge Langseth (ACML, 2023)

On Reporting Robust and Trustworthy Conclusions from Model Comparison Studies Involving Neural Networks and Randomness. - Odd Erik Gundersen; Saeid Shamsaliei; Håkon Sletten Kjærnli; Helge Langseth (ACM-REP, 2023)

mTADS: Multivariate Time Series Anomaly Detection Benchmark Suites. - David Baumgartner; Helge Langseth; Heri Ramampiaro; Kenth Engø-Monsen (IEEE Big Data, 2023)

Bayesian Exploration in Deep Reinforcement Learning. - Ludvig Killingberg; Helge Langseth (NAIS, 2023)

Providing Previously Unseen Users Fair Recommendations Using Variational Autoencoders. - Bjørnar Vassøy; Helge Langseth; Benjamin Kille (RecSys, 2023)

Deep Contextual Grid Triplet Network for Context-Aware Recommendation. - Sofia Aftab; Heri Ramampiaro; Helge Langseth; Massimiliano Ruocco (IEEE Access, 2023)

Lecture Notes in Probabilistic Diffusion Models. - Inga Strümke; Helge Langseth (CoRR, 2023)

The Multiquadric Kernel for Moment-Matching Distributional Reinforcement Learning. - Ludvig Killingberg; Helge Langseth (Trans. Mach. Learn. Res., 2023)

A Divide and Conquer Approach for Solving Structural Causal Models. - Anna Rodum Bjøru; Rafael Cabañas 001; Helge Langseth; Antonio Salmerón (PGM, 2024)

Consumer-side fairness in recommender systems: a systematic survey of methods and evaluation. - Bjørnar Vassøy; Helge Langseth (Artif. Intell. Rev., 2024)

Probing the Robustness of Time-series Forecasting Models with CounterfacTS. - Håkon Hanisch Kjærnli; Lluis Mas-Ribas; Aida Ashrafi; Gleb Sizov; Helge Langseth; Odd Erik Gundersen (CoRR, 2024)

Opt-in Transparent Fairness for Recommender Systems. - Bjørnar Vassøy; Benjamin Kille; Helge Langseth (ECIR (1), 2025)

Divide and conquer for causal computation. - Anna Rodum Bjøru; Rafael Cabañas 001; Helge Langseth; Antonio Salmerón (Int. J. Approx. Reason., 2025)